mirror of
https://github.com/hiyouga/LLaMA-Factory.git
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112 lines
4.7 KiB
Python
112 lines
4.7 KiB
Python
# Inspired by: https://github.com/lvwerra/trl/blob/main/examples/research_projects/stack_llama/scripts/rl_training.py
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import math
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from typing import TYPE_CHECKING, List, Optional
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from torch.optim import AdamW
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from transformers import DataCollatorWithPadding
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from transformers.optimization import get_scheduler
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from trl import PPOConfig
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from ...data import get_dataset
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from ...extras.callbacks import FixValueHeadModelCallback
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from ...extras.misc import fix_valuehead_checkpoint
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from ...extras.ploting import plot_loss
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from ...model import load_model, load_tokenizer
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from ..utils import create_custom_optimzer, create_custom_scheduler, create_ref_model, create_reward_model
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from .trainer import CustomPPOTrainer
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if TYPE_CHECKING:
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from transformers import Seq2SeqTrainingArguments, TrainerCallback
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from ...hparams import DataArguments, FinetuningArguments, GeneratingArguments, ModelArguments
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def run_ppo(
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model_args: "ModelArguments",
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data_args: "DataArguments",
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training_args: "Seq2SeqTrainingArguments",
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finetuning_args: "FinetuningArguments",
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generating_args: "GeneratingArguments",
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callbacks: Optional[List["TrainerCallback"]] = None,
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):
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tokenizer = load_tokenizer(model_args)
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dataset = get_dataset(tokenizer, model_args, data_args, training_args, stage="ppo")
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model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train, add_valuehead=True)
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tokenizer.padding_side = "left" # use left-padding in generation while using right-padding in training
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data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
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# Create reference model and reward model
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ref_model = create_ref_model(model_args, finetuning_args, add_valuehead=True)
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reward_model = create_reward_model(model, model_args, finetuning_args)
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# Create ppo config
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backward_batch_size = training_args.per_device_train_batch_size * training_args.gradient_accumulation_steps
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ppo_config = PPOConfig(
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model_name=model_args.model_name_or_path,
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learning_rate=training_args.learning_rate,
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mini_batch_size=training_args.per_device_train_batch_size,
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batch_size=backward_batch_size * finetuning_args.ppo_buffer_size,
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gradient_accumulation_steps=training_args.gradient_accumulation_steps,
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ppo_epochs=finetuning_args.ppo_epochs,
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max_grad_norm=training_args.max_grad_norm,
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seed=training_args.seed,
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optimize_device_cache=True,
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target=finetuning_args.ppo_target,
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log_with=finetuning_args.ppo_logger,
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use_score_scaling=finetuning_args.ppo_score_norm,
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use_score_norm=finetuning_args.ppo_score_norm,
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whiten_rewards=finetuning_args.ppo_whiten_rewards,
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accelerator_kwargs={"step_scheduler_with_optimizer": False},
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project_kwargs={"logging_dir": training_args.logging_dir},
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)
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# Create optimizer and scheduler
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if training_args.max_steps > 0:
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num_training_steps = training_args.max_steps
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else:
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total_train_batch_size = backward_batch_size * finetuning_args.ppo_buffer_size * training_args.world_size
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num_training_steps = training_args.num_train_epochs * math.ceil(len(dataset) / total_train_batch_size)
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optimizer = create_custom_optimzer(model, training_args, finetuning_args)
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create_custom_scheduler(training_args, num_training_steps, optimizer)
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if optimizer is None:
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optimizer = AdamW(filter(lambda p: p.requires_grad, model.parameters()), lr=training_args.learning_rate)
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lr_scheduler = get_scheduler(
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training_args.lr_scheduler_type,
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optimizer=optimizer,
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num_warmup_steps=training_args.get_warmup_steps(num_training_steps),
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num_training_steps=num_training_steps,
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)
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# Initialize our Trainer
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ppo_trainer = CustomPPOTrainer(
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model_args=model_args,
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training_args=training_args,
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finetuning_args=finetuning_args,
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generating_args=generating_args,
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callbacks=callbacks + [FixValueHeadModelCallback()],
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reward_model=reward_model,
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config=ppo_config,
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model=model,
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ref_model=ref_model,
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tokenizer=tokenizer,
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dataset=dataset,
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data_collator=data_collator,
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optimizer=optimizer,
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lr_scheduler=lr_scheduler,
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)
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# Training
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if training_args.do_train:
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ppo_trainer.ppo_train(resume_from_checkpoint=training_args.resume_from_checkpoint)
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ppo_trainer.save_model()
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if training_args.should_save:
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fix_valuehead_checkpoint(model, training_args.output_dir, training_args.save_safetensors)
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ppo_trainer.save_state() # must be called after save_model to have a folder
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if ppo_trainer.is_world_process_zero() and finetuning_args.plot_loss:
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plot_loss(training_args.output_dir, keys=["loss", "reward"])
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